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Unpublished Paper
Two-stage Decompositions for the Analysis of Functional Connectivity for fMRI With Application to Alzheimer's Disease Risk
COBRA Preprint Series
  • Brian S Caffo, Johns Hopkins University
  • Ciprian M. Crainiceanu, Bloomberg School of Public Health, Department of Biostatistics, Johns Hopkins
  • Guillermo Verduzco
  • Stewart H. Mostofsky, Johns Hopkins Kennedy Krieger Institute
  • Susan Spear-Bassett, Johns Hopkins Department of Psychiatry
  • James J Pekar, Kennedy Krieger Institute
Date of this Version
11-14-2009
Abstract
Functional connectivity is the study of correlations in measured neurophysiological signals. Altered functional connectivity has been shown to be associated with numerous diseases including Alzheimer's disease and mild cognitive impairment. In this manuscript we use a two-stage application of the singular value decomposition to obtain data driven population-level measures of functional connectivity in functional magnetic resonance imaging (fMRI). The method is computationally simple and amenable to high dimensional fMRI data with large numbers of subjects. Simulation studies suggest the ability of the decomposition methods to recover population brain networks and their associated loadings. We further demonstrate the utility of these decompositions in a case-control functional logistic regression model. The method is applied to a novel fMRI study of Alzheimer's disease risk under a verbal paired associates task. We found empirical evidence of alternative connectivity in clinically asymptomatic at-risk subjects when compared to controls. The relevant brain network loads primarily on the temporal lobe and overlaps significantly with the olfactory areas and temporal poles.
Disciplines
Citation Information
Brian S Caffo, Ciprian M. Crainiceanu, Guillermo Verduzco, Stewart H. Mostofsky, et al.. "Two-stage Decompositions for the Analysis of Functional Connectivity for fMRI With Application to Alzheimer's Disease Risk" (2009)
Available at: http://works.bepress.com/ciprian_crainiceanu/9/